Model fit

Column

Assumption checks

Error: The RStudio 'Plots' window is too small to show this set of plots. You may try one of the
  following steps to resolve this problem.
  
- To fix this issue, please make the window larger.
  
- If this doesn't help, try to reset your zoom settings. In RStudio, go to Menu "View >
  Actual Size" and then retry.
  
- If this still doesn't resolve your problems, you may check whether your apps are rescaled.
  On Windows, this can be done in the display settings (Start > Settings > System > Display,
  "Scale and layout"). Reduce the scaling and try again.
  
- Finally, you can try to decrease the base font-size of your theme before plotting. Load
  `library(ggplot2)` and run: `theme_set(theme_classic(base_size = 6))`

Column

Indices of model fit

Metric	Value
AIC	9.12e+05
AICc	9.12e+05
BIC	9.12e+05
R2	0.92
R2 (adj.)	0.92
RMSE	1129.84
Sigma	1130.09

For interpretation of performance metrics, please refer to this documentation.

Parameter estimates

Column

Plot

Column

Tabular summary

Parameter	Coefficient	SE	95% CI	t(53916)	p
(Intercept)	5753.76	396.63	(4976.36, 6531.16)	14.51	< .001
carat	11256.98	48.63	(11161.67, 11352.29)	231.49	< .001
cut (linear)	584.46	22.48	(540.40, 628.51)	26.00	< .001
cut (quadratic)	-301.91	17.99	(-337.18, -266.64)	-16.78	< .001
cut (cubic)	148.03	15.48	(117.69, 178.38)	9.56	< .001
cut (4th degree)	-20.79	12.38	(-45.05, 3.46)	-1.68	0.093
color (linear)	-1952.16	17.34	(-1986.15, -1918.17)	-112.57	< .001
color (quadratic)	-672.05	15.78	(-702.98, -641.13)	-42.60	< .001
color (cubic)	-165.28	14.72	(-194.14, -136.42)	-11.22	< .001
color (4th degree)	38.20	13.53	(11.68, 64.71)	2.82	0.005
color (5th degree)	-95.79	12.78	(-120.83, -70.75)	-7.50	< .001
color (6th degree)	-48.47	11.61	(-71.23, -25.70)	-4.17	< .001
clarity (linear)	4097.43	30.26	(4038.12, 4156.74)	135.41	< .001
clarity (quadratic)	-1925.00	28.23	(-1980.33, -1869.68)	-68.20	< .001
clarity (cubic)	982.20	24.15	(934.87, 1029.54)	40.67	< .001
clarity (4th degree)	-364.92	19.29	(-402.72, -327.12)	-18.92	< .001
clarity (5th degree)	233.56	15.75	(202.69, 264.44)	14.83	< .001
clarity (6th degree)	6.88	13.72	(-20.00, 33.77)	0.50	0.616
clarity (7th degree)	90.64	12.10	(66.92, 114.36)	7.49	< .001
depth	-63.81	4.53	(-72.69, -54.92)	-14.07	< .001
table	-26.47	2.91	(-32.18, -20.77)	-9.09	< .001
x	-1008.26	32.90	(-1072.74, -943.78)	-30.65	< .001
y	9.61	19.33	(-28.28, 47.50)	0.50	0.619
z	-50.12	33.49	(-115.75, 15.51)	-1.50	0.134

To find out more about table summary options, please refer to this documentation.

Predicted Values

Column

Plot

Error in Ops.data.frame(guide_loc, panel_loc): '==' only defined for equally-sized data frames

Column

Tabular summary

Model-based Expectation
carat	Predicted	SE	95% CI
-1.10	-21538.74	106.13	(-21746.75, -21330.73)
-0.62	-16202.81	86.54	(-16372.43, -16033.18)
-0.15	-10866.87	69.13	(-11002.37, -10731.37)
0.32	-5530.94	55.96	( -5640.62, -5421.26)
0.80	-195.01	50.46	( -293.91, -96.10)
1.27	5140.93	54.99	( 5033.15, 5248.70)
1.75	10476.86	67.55	( 10344.46, 10609.27)
2.22	15812.80	84.65	( 15646.88, 15978.72)
2.69	21148.73	104.07	( 20944.75, 21352.72)
3.17	26484.67	124.74	( 26240.18, 26729.15)

Variable predicted: price

Predictors modulated: carat

Predictors controlled: cut (1), color (1), clarity (1), depth (62), table (57), x (5.7), y (5.7), z (3.5)

Model-based Expectation
cut	Predicted	SE	95% CI
Fair	-195.01	50.46	(-293.91, -96.10)
Good	384.75	47.28	( 292.07, 477.42)
Very Good	531.78	45.79	( 442.03, 621.53)
Premium	567.14	45.51	( 477.93, 656.34)
Ideal	637.91	45.31	( 549.10, 726.71)

Variable predicted: price

Predictors modulated: cut

Predictors controlled: carat (0.8), color (1), clarity (1), depth (62), table (57), x (5.7), y (5.7), z (3.5)

Model-based Expectation
color	Predicted	SE	95% CI
D	-195.01	50.46	( -293.91, -96.10)
E	-404.12	49.75	( -501.63, -306.62)
F	-467.86	49.59	( -565.05, -370.67)
G	-677.04	49.64	( -774.34, -579.75)
H	-1175.27	49.98	(-1273.23, -1077.32)
I	-1661.25	51.17	(-1761.55, -1560.95)
J	-2564.40	53.43	(-2669.13, -2459.67)

Variable predicted: price

Predictors modulated: color

Predictors controlled: carat (0.8), cut (1), clarity (1), depth (62), table (57), x (5.7), y (5.7), z (3.5)

Model-based Expectation
clarity	Predicted	SE	95% CI
I1	-195.01	50.46	(-293.91, -96.10)
SI2	2507.58	34.78	(2439.42, 2575.74)
SI1	3470.47	34.60	(3402.65, 3538.29)
VS2	4072.22	34.94	(4003.73, 4140.70)
VS1	4383.39	35.93	(4312.96, 4453.82)
VVS2	4755.81	37.37	(4682.56, 4829.06)
VVS1	4812.75	39.18	(4735.96, 4889.55)
IF	5150.10	43.70	(5064.44, 5235.75)

Variable predicted: price

Predictors modulated: clarity

Predictors controlled: carat (0.8), cut (1), color (1), depth (62), table (57), x (5.7), y (5.7), z (3.5)

Model-based Expectation
depth	Predicted	SE	95% CI
56.02	170.63	61.45	( 50.19, 291.08)
57.45	79.22	57.81	( -34.09, 192.54)
58.88	-12.19	54.70	(-119.40, 95.03)
60.32	-103.60	52.22	(-205.95, -1.24)
61.75	-195.01	50.46	(-293.91, -96.10)
63.18	-286.42	49.49	(-383.43, -189.41)
64.61	-377.83	49.37	(-474.60, -281.05)
66.05	-469.24	50.10	(-567.43, -371.04)
67.48	-560.65	51.64	(-661.86, -459.43)
68.91	-652.06	53.92	(-757.75, -546.36)

Variable predicted: price

Predictors modulated: depth

Predictors controlled: carat (0.8), cut (1), color (1), clarity (1), table (57), x (5.7), y (5.7), z (3.5)

Model-based Expectation
table	Predicted	SE	95% CI
48.52	41.62	60.57	( -77.10, 160.34)
50.75	-17.54	57.11	(-129.46, 94.39)
52.99	-76.69	54.20	(-182.93, 29.55)
55.22	-135.85	51.96	(-237.69, -34.01)
57.46	-195.01	50.46	(-293.91, -96.10)
59.69	-254.16	49.77	(-351.72, -156.61)
61.93	-313.32	49.93	(-411.18, -215.45)
64.16	-372.47	50.92	(-472.29, -272.66)
66.40	-431.63	52.71	(-534.94, -328.32)
68.63	-490.79	55.21	(-599.00, -382.58)

Variable predicted: price

Predictors modulated: table

Predictors controlled: carat (0.8), cut (1), color (1), clarity (1), depth (62), x (5.7), y (5.7), z (3.5)

Model-based Expectation
x	Predicted	SE	95% CI
1.24	4329.11	156.84	( 4021.70, 4636.51)
2.37	3198.08	122.47	( 2958.03, 3438.12)
3.49	2067.05	90.14	( 1890.38, 2243.72)
4.61	936.02	63.04	( 812.47, 1059.57)
5.73	-195.01	50.46	( -293.91, -96.10)
6.85	-1326.03	61.99	(-1447.53, -1204.53)
7.97	-2457.06	88.67	(-2630.86, -2283.26)
9.10	-3588.09	120.86	(-3824.97, -3351.21)
10.22	-4719.12	155.16	(-5023.23, -4415.01)
11.34	-5850.14	190.43	(-6223.40, -5476.89)

Variable predicted: price

Predictors modulated: x

Predictors controlled: carat (0.8), cut (1), color (1), clarity (1), depth (62), table (57), y (5.7), z (3.5)

Model-based Expectation
y	Predicted	SE	95% CI
1.17	-238.90	100.51	(-435.91, -41.89)
2.31	-227.93	82.17	(-388.97, -66.88)
3.45	-216.95	66.14	(-346.59, -87.32)
4.59	-205.98	54.52	(-312.85, -99.11)
5.73	-195.01	50.46	(-293.91, -96.10)
6.88	-184.03	55.63	(-293.07, -74.99)
8.02	-173.06	67.96	(-306.26, -39.85)
9.16	-162.08	84.36	(-327.44, 3.27)
10.30	-151.11	102.91	(-352.82, 50.61)
11.45	-140.13	122.64	(-380.51, 100.24)

Variable predicted: price

Predictors modulated: y

Predictors controlled: carat (0.8), cut (1), color (1), clarity (1), depth (62), table (57), x (5.7), z (3.5)

Model-based Expectation
z	Predicted	SE	95% CI
0.72	-53.53	106.75	(-262.75, 155.69)
1.42	-88.90	86.64	(-258.72, 80.93)
2.13	-124.27	68.82	(-259.16, 10.63)
2.83	-159.64	55.53	(-268.47, -50.81)
3.54	-195.01	50.46	(-293.91, -96.10)
4.24	-230.37	55.91	(-339.96, -120.78)
4.95	-265.74	69.45	(-401.86, -129.62)
5.66	-301.11	87.39	(-472.40, -129.83)
6.36	-336.48	107.55	(-547.29, -125.68)
7.07	-371.85	128.90	(-624.49, -119.20)

Variable predicted: price

Predictors modulated: z

Predictors controlled: carat (0.8), cut (1), color (1), clarity (1), depth (62), table (57), x (5.7), y (5.7)

Text reports

Column

Textual summary

We fitted a linear model (estimated using OLS) to predict price with carat, cut, color, clarity, depth, table, x, y and z (formula: price ~ carat + cut + color + clarity + depth + table + x + y + z). The model explains a statistically significant and substantial proportion of variance (R2 = 0.92, F(23, 53916) = 26881.83, p < .001, adj. R2 = 0.92). The model’s intercept, corresponding to carat = 0, cut = , color = , clarity = , depth = 0, table = 0, x = 0, y = 0 and z = 0, is at 5753.76 (95% CI (4976.36, 6531.16), t(53916) = 14.51, p < .001). Within this model:

The effect of carat is statistically significant and positive (beta = 11256.98, 95% CI (11161.67, 11352.29), t(53916) = 231.49, p < .001; Std. beta = 1.34, 95% CI (1.33, 1.35))
The effect of cut (linear) is statistically significant and positive (beta = 584.46, 95% CI (540.40, 628.51), t(53916) = 26.00, p < .001; Std. beta = 0.15, 95% CI (0.14, 0.16))
The effect of cut (quadratic) is statistically significant and negative (beta = -301.91, 95% CI (-337.18, -266.64), t(53916) = -16.78, p < .001; Std. beta = -0.08, 95% CI (-0.08, -0.07))
The effect of cut (cubic) is statistically significant and positive (beta = 148.03, 95% CI (117.69, 178.38), t(53916) = 9.56, p < .001; Std. beta = 0.04, 95% CI (0.03, 0.04))
The effect of cut (4th degree) is statistically non-significant and negative (beta = -20.79, 95% CI (-45.05, 3.46), t(53916) = -1.68, p = 0.093; Std. beta = -5.21e-03, 95% CI (-0.01, 8.68e-04))
The effect of color (linear) is statistically significant and negative (beta = -1952.16, 95% CI (-1986.15, -1918.17), t(53916) = -112.57, p < .001; Std. beta = -0.49, 95% CI (-0.50, -0.48))
The effect of color (quadratic) is statistically significant and negative (beta = -672.05, 95% CI (-702.98, -641.13), t(53916) = -42.60, p < .001; Std. beta = -0.17, 95% CI (-0.18, -0.16))
The effect of color (cubic) is statistically significant and negative (beta = -165.28, 95% CI (-194.14, -136.42), t(53916) = -11.22, p < .001; Std. beta = -0.04, 95% CI (-0.05, -0.03))
The effect of color (4th degree) is statistically significant and positive (beta = 38.20, 95% CI (11.68, 64.71), t(53916) = 2.82, p = 0.005; Std. beta = 9.57e-03, 95% CI (2.93e-03, 0.02))
The effect of color (5th degree) is statistically significant and negative (beta = -95.79, 95% CI (-120.83, -70.75), t(53916) = -7.50, p < .001; Std. beta = -0.02, 95% CI (-0.03, -0.02))
The effect of color (6th degree) is statistically significant and negative (beta = -48.47, 95% CI (-71.23, -25.70), t(53916) = -4.17, p < .001; Std. beta = -0.01, 95% CI (-0.02, -6.44e-03))
The effect of clarity (linear) is statistically significant and positive (beta = 4097.43, 95% CI (4038.12, 4156.74), t(53916) = 135.41, p < .001; Std. beta = 1.03, 95% CI (1.01, 1.04))
The effect of clarity (quadratic) is statistically significant and negative (beta = -1925.00, 95% CI (-1980.33, -1869.68), t(53916) = -68.20, p < .001; Std. beta = -0.48, 95% CI (-0.50, -0.47))
The effect of clarity (cubic) is statistically significant and positive (beta = 982.20, 95% CI (934.87, 1029.54), t(53916) = 40.67, p < .001; Std. beta = 0.25, 95% CI (0.23, 0.26))
The effect of clarity (4th degree) is statistically significant and negative (beta = -364.92, 95% CI (-402.72, -327.12), t(53916) = -18.92, p < .001; Std. beta = -0.09, 95% CI (-0.10, -0.08))
The effect of clarity (5th degree) is statistically significant and positive (beta = 233.56, 95% CI (202.69, 264.44), t(53916) = 14.83, p < .001; Std. beta = 0.06, 95% CI (0.05, 0.07))
The effect of clarity (6th degree) is statistically non-significant and positive (beta = 6.88, 95% CI (-20.00, 33.77), t(53916) = 0.50, p = 0.616; Std. beta = 1.73e-03, 95% CI (-5.01e-03, 8.46e-03))
The effect of clarity (7th degree) is statistically significant and positive (beta = 90.64, 95% CI (66.92, 114.36), t(53916) = 7.49, p < .001; Std. beta = 0.02, 95% CI (0.02, 0.03))
The effect of depth is statistically significant and negative (beta = -63.81, 95% CI (-72.69, -54.92), t(53916) = -14.07, p < .001; Std. beta = -0.02, 95% CI (-0.03, -0.02))
The effect of table is statistically significant and negative (beta = -26.47, 95% CI (-32.18, -20.77), t(53916) = -9.09, p < .001; Std. beta = -0.01, 95% CI (-0.02, -0.01))
The effect of x is statistically significant and negative (beta = -1008.26, 95% CI (-1072.74, -943.78), t(53916) = -30.65, p < .001; Std. beta = -0.28, 95% CI (-0.30, -0.27))
The effect of y is statistically non-significant and positive (beta = 9.61, 95% CI (-28.28, 47.50), t(53916) = 0.50, p = 0.619; Std. beta = 2.75e-03, 95% CI (-8.10e-03, 0.01))
The effect of z is statistically non-significant and negative (beta = -50.12, 95% CI (-115.75, 15.51), t(53916) = -1.50, p = 0.134; Std. beta = -8.87e-03, 95% CI (-0.02, 2.74e-03))

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation. The model explains a statistically significant and substantial proportion of variance (R2 = 0.92, F(23, 53916) = 26881.83, p < .001, adj. R2 = 0.92)

Column

Model information

Error in (function (..., row.names = NULL, check.rows = FALSE, check.names = TRUE, : arguments imply differing number of rows: 1, 0

List of 48
 $ is_binomial     : logi FALSE
 $ is_bernoulli    : logi FALSE
 $ is_count        : logi FALSE
 $ is_poisson      : logi FALSE
 $ is_negbin       : logi FALSE
 $ is_beta         : logi FALSE
 $ is_betabinomial : logi FALSE
 $ is_orderedbeta  : logi FALSE
 $ is_dirichlet    : logi FALSE
 $ is_exponential  : logi FALSE
 $ is_logit        : logi FALSE
 $ is_probit       : logi FALSE
 $ is_censored     : logi FALSE
 $ is_truncated    : logi FALSE
 $ is_survival     : logi FALSE
 $ is_linear       : logi TRUE
 $ is_tweedie      : logi FALSE
 $ is_zeroinf      : logi FALSE
 $ is_zero_inflated: logi FALSE
 $ is_dispersion   : logi FALSE
 $ is_hurdle       : logi FALSE
 $ is_ordinal      : logi FALSE
 $ is_cumulative   : logi FALSE
 $ is_multinomial  : logi FALSE
 $ is_categorical  : logi FALSE
 $ is_mixed        : logi FALSE
 $ is_multivariate : logi FALSE
 $ is_trial        : logi FALSE
 $ is_bayesian     : logi FALSE
 $ is_gam          : logi FALSE
 $ is_anova        : logi FALSE
 $ is_timeseries   : logi FALSE
 $ is_ttest        : logi FALSE
 $ is_correlation  : logi FALSE
 $ is_onewaytest   : logi FALSE
 $ is_chi2test     : logi FALSE
 $ is_ranktest     : logi FALSE
 $ is_levenetest   : logi FALSE
 $ is_variancetest : logi FALSE
 $ is_xtab         : logi FALSE
 $ is_proptest     : logi FALSE
 $ is_binomtest    : logi FALSE
 $ is_ftest        : logi FALSE
 $ is_meta         : logi FALSE
 $ link_function   : chr "identity"
 $ family          : chr "gaussian"
 $ n_obs           : int 53940
 $ n_grouplevels   : NULL

Error in DT::datatable(model_info_data): 'data' must be 2-dimensional (e.g. data frame or matrix)

---
title: "Regression model summary from `{easystats}`"
output: 
  flexdashboard::flex_dashboard:
    theme:
      version: 4
      # bg: "#101010"
      # fg: "#FDF7F7" 
      primary: "#0054AD"
      base_font:
        google: Prompt
      code_font:
        google: JetBrains Mono
params:
  model: model
  check_model_args: check_model_args
  parameters_args: parameters_args
  performance_args: performance_args
---

```{r setup, include=FALSE}
library(flexdashboard)
library(easystats)

# Since not all regression model are supported across all packages, make the
# dashboard chunks more fault-tolerant. E.g. a model might be supported in
# `{parameters}`, but not in `{report}`.
#
# For this reason, `error = TRUE`
knitr::opts_chunk$set(
  error = TRUE,
  out.width = "100%"
)

# helper function for printing `{report}` outputs
bracket_to_parantheses <- function(text) {
  gsub("]", ")", gsub("[", "(", text, fixed = TRUE), fixed = TRUE)
}
```

```{r easydashboard-1}
# Get user-specified model data
model <- params$model

# Is it supported by `{easystats}`? Skip evaluation of the following chunks if not.
is_supported <- insight::is_model_supported(model)

if (!is_supported) {
  unsupported_message <- sprintf(
    "Unfortunately, objects of class `%s` are not yet supported in {easystats}.\n
    For a list of supported models, see `insight::supported_models()`.",
    class(model)[1]
  )
}
```


Model fit 
=====================================  

Column {data-width=700}
-----------------------------------------------------------------------

### Assumption checks

```{r check-model, eval=is_supported, fig.height=10, fig.width=10}
check_model_args <- c(list(model), params$check_model_args)
# add verbose, if not done yet
if (is.null(check_model_args$verbose)) check_model_args$verbose <- FALSE
tryCatch(
  {
    do.call(performance::check_model, check_model_args)
  },
  error = function(e) {
    cat(insight::format_message(
      "\nSomething did not work as expected. Please file an issue at {.url https://github.com/easystats/easystats/issues/} and post the following output:",
      paste0("\n`", e$message, "`")
    ))
  }
)
```

```{r easydashboard-2, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=300}
-----------------------------------------------------------------------

### Indices of model fit

```{r easydashboard-3, eval=is_supported}
# {performance}
performance_args <- c(list(model), params$performance_args)
# add verbose, if not done yet
if (is.null(performance_args$verbose)) performance_args$verbose <- FALSE
table_performance <- do.call(performance::performance, performance_args)
print_md(table_performance, layout = "vertical", caption = NULL)
```


```{r easydashboard-4, eval=!is_supported}
cat(unsupported_message)
```

For interpretation of performance metrics, please refer to <a href="https://easystats.github.io/performance/reference/model_performance.html" target="_blank">this documentation</a>.

Parameter estimates
=====================================  

Column {data-width=550}
-----------------------------------------------------------------------

### Plot

```{r dot-whisker, eval=is_supported}
# `{parameters}`
parameters_args <- c(list(model), params$parameters_args)
# add verbose, if not done yet
if (is.null(parameters_args$verbose)) parameters_args$verbose <- FALSE
table_parameters <- do.call(parameters::parameters, parameters_args)

plot(table_parameters)
```


```{r easydashboard-5, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=450}
-----------------------------------------------------------------------

### Tabular summary

```{r easydashboard-6, eval=is_supported}
print_md(table_parameters, caption = NULL)
```


```{r easydashboard-7, eval=!is_supported}
cat(unsupported_message)
```

To find out more about table summary options, please refer to <a href="https://easystats.github.io/parameters/reference/model_parameters.html" target="_blank">this documentation</a>.


Predicted Values
=====================================  

Column {data-width=600}
-----------------------------------------------------------------------

### Plot

```{r expected-values, eval=is_supported, fig.height=10, fig.width=10}
# {modelbased}
int_terms <- find_interactions(model, component = "conditional", flatten = TRUE)
con_terms <- find_variables(model)$conditional

if (is.null(int_terms)) {
  model_terms <- con_terms
} else {
  model_terms <- clean_names(int_terms)
  int_terms <- unique(unlist(strsplit(clean_names(int_terms), ":", fixed = TRUE)))
  model_terms <- c(model_terms, setdiff(con_terms, int_terms))
}

# check some exceptions here: logistic regression models with factor response
# usually require the response to be included in the model, else `get_modelmatrix()`
# fails, which is required to compute SE/CI for `get_predicted()`
response <- find_response(model)
minfo <- model_info(model)
model_data <- get_data(model)
include_response <- minfo$is_binomial && minfo$is_logit && is.factor(model_data[[response]])

text_modelbased <- tryCatch(
  {
    lapply(unique(model_terms), function(i) {
      grid <- get_datagrid(
        model,
        at = i,
        range = "grid",
        preserve_range = FALSE,
        verbose = FALSE,
        include_response = include_response
      )
      estimate_expectation(model, data = grid, verbose = FALSE)
    })
  },
  error = function(e) {
    cat(insight::format_message(
      "\nSomething did not work as expected. Please file an issue at {.url https://github.com/easystats/easystats/issues/} and post the following output:",
      paste0("\n`", e$message, "`")
    ))
    NULL
  }
)

if (!is.null(text_modelbased)) {
  ggplot2::theme_set(theme_modern())
  # all_plots <- lapply(text_modelbased, function(i) {
  #   out <- do.call(visualisation_recipe, c(list(i), modelbased_args))
  #   plot(out) + ggplot2::ggtitle("")
  # })
  all_plots <- lapply(text_modelbased, function(i) {
    out <- visualisation_recipe(i, show_data = "none")
    plot(out) + ggplot2::ggtitle("")
  })

  see::plots(all_plots, n_columns = round(sqrt(length(text_modelbased))))
}
```


```{r easydashboard-8, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=400}
-----------------------------------------------------------------------

### Tabular summary

```{r easydashboard-9, eval=is_supported, results="asis"}
if (!is.null(text_modelbased)) {
  for (i in text_modelbased) {
    tmp <- print_md(i)
    tmp <- gsub("Variable predicted", "\nVariable predicted", tmp, fixed = TRUE)
    tmp <- gsub("Predictors modulated", "\nPredictors modulated", tmp, fixed = TRUE)
    tmp <- gsub("Predictors controlled", "\nPredictors controlled", tmp, fixed = TRUE)
    print(tmp)
  }
}
```


```{r easydashboard-10, eval=!is_supported}
cat(unsupported_message)
```


Text reports
=====================================    

Column {data-width=500}
-----------------------------------------------------------------------

### Textual summary

```{r easydashboard-11, eval=is_supported, results='asis', collapse=TRUE}
# {report}
text_report <- tryCatch(
  {
    report(model, verbose = FALSE)
  },
  error = function(e) {
    cat(insight::format_message(
      "\nSomething did not work as expected. Please file an issue at {.url https://github.com/easystats/easystats/issues/} and post the following output:",
      paste0("\n`", e$message, "`")
    ))
    NULL
  }
)


text_report_performance <- tryCatch(
  {
    report_performance(model, verbose = FALSE)
  },
  error = function(e) {
    cat(insight::format_message(
      "\nSomething did not work as expected. Please file an issue at {.url https://github.com/easystats/easystats/issues/} and post the following output:",
      paste0("\n`", e$message, "`")
    ))
    NULL
  }
)

if (!is.null(text_report)) {
  cat(bracket_to_parantheses(text_report))
  cat("\n")
}

if (!is.null(text_report_performance)) {
  cat(bracket_to_parantheses(text_report_performance))
}
```


```{r easydashboard-12, eval=!is_supported}
cat(unsupported_message)
```

Column {data-width=500}
-----------------------------------------------------------------------

### Model information

```{r easydashboard-13, eval=is_supported}
model_info_data <- insight::model_info(model, verbose = FALSE)
model_info_data <- datawizard::data_to_long(as.data.frame(model_info_data))

DT::datatable(model_info_data)
```

```{r easydashboard-14, eval=!is_supported}
cat(unsupported_message)
```